Next date: May 2026
Time: TBD
Prerequisites: No knowledge of statistical software is required.

Overview: Power calculations and sample size determination are essential parts of planning a scientific study. In this course (run over two days), we will introduce the basic principles of precision-based and power-based sample size calculations. Using practical examples, we will demonstrate how to perform sample size calculations for common designs in single-sample studies and for comparing groups, as well as discussing practical issues such as multiple comparisons.

Next dates: February and May 2026
Time: TBD
Prerequisites: Participants must have basic R skills prior to workshop

Overview: This workshop is designed as an introduction to statistical analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. It is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this workshop can serve as a first introduction or a refresher. The theory behind the statistical procedures will, in general, not be discussed.

Next date: February 2026
Time: TBD
Prerequisites: We assume knowledge of introductory statistics (see description below for more details).

Overview: This course provides a comprehensive hands-on introduction to regression analysis techniques. Content is designed for researchers with some prior knowledge of basic statistical testing, such as t-tests, p-values, confidence intervals and simple linear regression. The primary focus is on developing a conceptual understanding of regression models through numerous examples. There will be a strong emphasis on practical implementation in R, and interpretation of output. Approximately half the time will be dedicated to practical hands-on sessions.

Next date: May 2026
Time: TBD
Prerequisites: None

Overview: Simple statistical methods (t-tests, chi-square tests, linear models/regression) assume independence of observations and cannot be used when dependence is present in the sample.

Common reasons for dependencies include:

• Clustering e.g. multiple patients per hospital, multiple plants per site, multiple measurements on the same individual.
• Time and space - measurements taken close in time and space are more similar.

Mixed models are extensions of linear models to dependent data. The course will explain how to fit, check, interpret and communicate results from mixed models.

Next date: July 2026
Time: TBD
Prerequisites: Intermediate R users (have been using R for at least 6 months, comfortable loading data, using basic ggplot and dplyr)

Overview: In this course, we teach students techniques for scaling up their analyses. How do you get R to repeat tasks in an efficient way? This general skill is needed in diverse contexts, such as processing lots of files (e.g. from imaging or surveys), working with large datasets, or simulations (statistical or individual-based).

In these circumstances, you'll often find yourself having to repeat the same thing over and over and it's good to learn how to get R to do this in an organised and efficient manner.

Date: TBD
Time: TBD
Prerequisites: RStudio and Regression (with a single response variable) are assumed knowledge. 

Overview: This workshop extends on the Fundamentals of Regression course and introduces multivariate statistics in a model based framework. While particularly relevant to ecologists, these methods have valuable applications across multiple disciplines. We move beyond a single response variable to visualising and analysing a collection of correlated response variables.